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    Opportunity: Associate Director of Machine Learning, Quantitative Biology @ One of the world's leading pharmaceutical companies -- Gothenburg, Sweden
    Submitted by Eugene Mc Daid; posted on Monday, May 14, 2018

    Submitter

    BACKGROUND

    One of the world's leading pharmaceutical companies is now seeking an exceptionally talented Machine Learning Scientist to join their Discovery Sciences team as an Associate Director. This coveted position will be responsible for devising machine learning-based departmental strategy to drive target discovery and support drug development across core therapeutic fields.

    RESPONSIBILITIES

    Main responsibilities of the Associate Director of Machine Learning will be:
    • You will lead the machine learning discipline, develop and implement machine learning strategies for target identification
    • Work collaboratively with internal drug discovery projects, therapeutic divisions and platform teams, using expertise in machine learning and statistical modelling to address key biological questions and deliver solutions
    • Establish and nurture academic collaborations to drive AI/machine learning based genomic research

    REQUIREMENTS

    Ideal qualities of the Associate Director of Machine Learning would be:
    • An excellent academic background, with a PhD in Mathematics, Quantitative Biology, Statistics, Computer Science or a related discipline
    • Experience managing a multi-disciplinary team of statisticians, data scientists, bioinformaticians etc.
    • Experience working with genomic data (NGS, WGS, WES) and image data is ideal
    • Experience applying machine learning to target discovery sciences/pre-clinical
    • Expertise in one or several of the core machine learning areas, eg. Artificial Neural Networks, Supervised/Unsupervised learning methods, Support Vector Machines, Bayesian approaches, Gaussian processes, Markov Models, Decision Theory etc.
    • Strong technical ability, excellent programming skills with expert use of Python sand R, as well as relevant frameworks such as scikit-learn & Tensorflow
    • Good understanding of molecular biology, cell biology and genomics

    HOW TO APPLY

    This is a truly exciting opportunity to work on cutting edge science, and use your skills to transform millions of lives by supporting the development of new pharmaceuticals. For more information about this opportunity, email your CV to Emilie Francis at efrancis[at]pararecruit.com. For an informal chat to discuss the position, call Emilie on 0121 616 3477.

    Key words:
    Data Science, Quantitive biology, machine learning, Bioinformatics, Drug Discovery, Target Identification, Discovery sciences, Pre-clinical, Artificial intelligence, Pharmaceutical, Pharma, Genomics, Statistics

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